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Yesterdays Number

by io.github.pipeworx-io

Ask Pipeworx — Grounded

ask_pipeworx_grounded
Read-onlyIdempotent

Hallucination-resistant answer mode for high-stakes reads. Same routing as ask_pipeworx — picks the right tool from 5,801 across 1517 sources, fills arguments, fetches the data — then EXTRACTS the answer using ONLY what the tool result contains. Returns {answer, evidence (verbatim quote), confidence, source, fetched_at, refusal_reason:null} on success, OR an explicit refusal {answer:null, refusal_reason:"not_in_source"|"no_tool_match"|"tool_error"|"data_truncated"|"llm_error"} when the data doesn't directly answer. Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts (financial verdicts, legal claims, medical lookups, public statements). Costs one extra LLM call vs ask_pipeworx — prefer ask_pipeworx for casual lookups.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoAlias for question.
textNoAlias for question.
inputNoAlias for question.
queryNoAlias for question.
promptNoAlias for question.
questionYesYour question in natural language. Accepts query, q, prompt, text, input as aliases.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.7/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare read-only, open-world, idempotent, and non-destructive behavior. The description adds relevant behavioral detail: it refuses with specific reason codes when the data does not directly answer and does not invent facts. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is repetitive and longer than necessary, repeating the 'same routing' explanation and the anti-hallucination constraint multiple times. It is organized and understandable, but could be trimmed significantly without losing information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Even without an output schema, the description fully specifies success and refusal return shapes, refusal reason codes, the cost trade-off versus ask_pipeworx, and appropriate use cases. Provides enough context for an agent to decide when to call and what to expect.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema covers all six parameters and each alias is described. The question parameter description adds meaning by stating it takes natural language and listing all accepted aliases, going slightly beyond the schema's per-alias descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly identifies the tool as a grounded answer mode that extracts answers only from tool results, explicitly contrasting with ask_pipeworx. The purpose—hallucination-resistant answering for high-stakes reads—is unmistakable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use the tool: whenever an answer will be quoted, cited, or acted on and must not invent facts. It also gives a concrete when-not-to-use rule by preferring ask_pipeworx for casual lookups due to the extra LLM call.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.9/5.0
Disambiguation3/5

Many tools have overlapping purposes, such as multiple query tools (ask_pipeworx, ask_pipeworx_grounded, deep_research) and prediction market analysis tools (bet_research, polymarket_arbitrage, polymarket_edges). While descriptions help differentiate, the clustering of similar functions may cause confusion.

Naming Consistency4/5

All tools use snake_case, but the pattern varies: some start with verbs (forget, remember, recall) while others start with nouns (entity_profile, pipeworx_trending). The naming is generally clear with minor inconsistencies.

Tool Count4/5

With 31 tools, the set is slightly large but appropriate given the broad scope covering company data, prediction markets, memory, subscriptions, and more. A few tools like yesterdays_number_get seem out of place, but overall the count is reasonable.

Completeness3/5

The tool set covers many domains comprehensively (SEC, FDA, FRED, prediction markets), but there are notable gaps such as no direct web search or stock price tool beyond routed queries. The server feels like a collection of capabilities rather than a unified domain.

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